Breast cancer in women is a serious health problem, the American Cancer Society currently estimating that over 180,000 U.S. women are diagnosed with breast cancer each year. Breast cancer is the second major cause of cancer death among women, the American Cancer Society also estimating that breast cancer causes the death of over 44,000 U.S. women each year. While at present there is no means for preventing breast cancer, early detection of the disease prolongs life expectancy and decreases the likelihood of the need for a total mastectomy. Mammography using x-rays is currently the most common method of detecting and analyzing breast lesions.
Recently, computer-aided diagnosis (CAD) systems have been developed for assisting the radiologist in the early detection of abnormal lesions or other suspicious masses in digital mammograms. An example of such a system is disclosed in "Method and Apparatus for Fast Detection of Spiculated Lesions in Digital Mammograms," U.S. patent application Ser. No. 08/676,660, filed Jul. 10, 1996 and assigned to the assignee of the present invention. The contents of the above disclosure are hereby incorporated by reference into the present disclosure. The developed CAD systems digitize x-ray mammograms to produce a digital mammogram, and perform numerical image processing algorithms on the digital mammogram. The output of such a CAD system is, for example, a highlighted display capable of directing the attention of a radiologist to suspicious portions of the x-ray mammogram.
FIGS. 1A and 1B show digital mammograms taken from the mediolateral oblique ("MLO") view using methods known in the art. FIG. 1A shows a digital mammogram 100 generally comprising three major components: a background 102, a breast tissue portion 104, and a pectoral muscle portion 106. As shown in FIG. 1A, the pectoral muscle portion 106 lies to the upper left of the breast tissue portion 104, and a generally linear boundary is formed between the two portions. In a digital mammogram of the opposite breast, the pectoral muscle portion would lie to the upper right of the breast tissue portion in a roughly symmetric fashion. Importantly, both the MLO view and another common view, the lateral view, yield mammograms having breast tissue portions near the center of the frame and pectoral muscle portions generally near the corner of the frame.
It is a desirable first step in the numerical processing of the digital mammogram 100 to segment the pectoral muscle portion 106 from the breast tissue portion 104. By segmenting it is meant that parameters are derived to describe the effective boundary between the two portions for use in subsequent image processing algorithms. FIG. 1C shows such a pectoral boundary 108 for the digital mammogram of FIG. 1A, while FIG. 1D shows such a pectoral boundary 108 for the digital mammogram of FIG. 1B. For most practical purposes, the pectoral boundary 108 can be described as a straight line having a slope parameter A.sub.P and and an intercept parameter B.sub.P, as shown in the coordinate system of FIG. 1E. Alternatively, the pectoral boundary 108 can be described by an offset .rho..sub.P and an angle .theta..sub.P, as shown in FIG. 1E.
Segmentation or identification of the pectoral boundary 108 assists subsequent CAD processing by enabling the use of further image processing techniques inside the breast tissue and pectoral muscle portions. As a first example, knowledge of the pectoral boundary 108 assists in improving contrast within the breast tissue area by permitting "blocking out" of the pectoral region. As a second example, because the mammogram is a projection image, there may be some breast or glandular tissue overlapping the muscle near the pectoral boundary 108. Knowledge of the location of the pectoral boundary 108 can form the basis for special localized algorithms for detecting certain cancers, e.g. in lymph nodes, which would not be detected using the standard breast tissue algorithms. Finally, segmentation of the pectoral muscle by identification of the pectoral boundary 108 is an important preprocessing step for automated comparisons of images of the left and right breast, for detection of cancers revealed by asymmetry. In particular, knowledge of the pectoral boundary assists in precise registration of the left and right breast images.
One known method of automated mammogram segmentation is described in Suckling et. al., "Segmentation of Mammograms Using Multiple Linked Self-Organizing Neural Networks," Med. Phys. 22 (2), February 1995, pp. 145-152. This method, based on self-organizing topological map networks, is limited in that the neural network is separately trained on each mammogram based on a prior estimate of an initial training region in that mammogram. Unfortunately, the location of the training region is difficult when the location of the pectoral muscle is itself uncertain. Additionally, the training process is relatively slow, reducing throughput of the overall CAD system.
Accordingly, it is an object of the present invention to provide a fast method for automatic segmentation of the pectoral muscle portion from the breast tissue portion of a digital mammogram.
It is a further object of the present invention to provide a accurate identification of the pectoral boundary with minimal prior knowledge of the specific location or extent of the pectoral muscle in that digital mammogram.
It is still a further object of the present invention to provide a method for automatic segmentation of the pectoral muscle which is more robust against variations in the dynamic range and average gray scale levels of the pectoral and breast tissue portions of digital mammograms.